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\title{Intelligent Intruder Avoidance for Wireless Sensor Networks}
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\author{
\alignauthor 
Taranjeet Singh Bhatia, Gurkan Solmaz and Gita Sukthankar\\
       \affaddr{Department of Electrical Engineering and Computer Science}\\
       \affaddr{University of Central Florida}\\
       \affaddr{4000 Central Florida Blvd, Orlando FL 32816}\\
       \email{\{tsbhatia,gsolmaz,gitars\}@eecs.ucf.edu}
}
\date{May 2012}
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\begin{abstract}
There are numerous real life applications of wireless sensor networks (WSN) in military and civilian fields. In such networks the route carriers sensitive data that are vulnerable to snooping or threats due to intrusions. Some malicious nodes within the existing networks may be compromised. While a number of techniques have been proposed in the literature for intrusion detection and avoidance, there has been no study that has systematically exploited the impact of neuroevolutionary algorithms application in intrusion avoidance of WSN. In this project, neuroevolution methodologies are used in advanced intrusion avoidance in WSN. We propose an attack scenario in which there are intrusion attacks on network from the adverse to corrupt or collect some data by disjoining the communication paths (or by destroying the relay nodes). By relay nodes, we define those set of nodes which act as a bridges within the network for forwarding the data through different parts of the networks. These nodes are the ones which, if disconnected, can cause disruptions and loss of sensitive data. We propose the use of neuroevolutionary algorithm of NEAT for the optimum path selection in multi-hop routing. We plan to use NEAT in such a manner that it would learn how to avoid such attack during its evolutionary phase and we would use different test-bed (via simulation) to see the performance of the evolved system.



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